Few-shot Learning for Multi-label Intent Detection
- URL: http://arxiv.org/abs/2010.05256v1
- Date: Sun, 11 Oct 2020 14:42:18 GMT
- Title: Few-shot Learning for Multi-label Intent Detection
- Authors: Yutai Hou, Yongkui Lai, Yushan Wu, Wanxiang Che, Ting Liu
- Abstract summary: State-of-the-art work estimates label-instance relevance scores and uses a threshold to select multiple associated intent labels.
Experiments on two datasets show that the proposed model significantly outperforms strong baselines in both one-shot and five-shot settings.
- Score: 59.66787898744991
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we study the few-shot multi-label classification for user
intent detection. For multi-label intent detection, state-of-the-art work
estimates label-instance relevance scores and uses a threshold to select
multiple associated intent labels. To determine appropriate thresholds with
only a few examples, we first learn universal thresholding experience on
data-rich domains, and then adapt the thresholds to certain few-shot domains
with a calibration based on nonparametric learning. For better calculation of
label-instance relevance score, we introduce label name embedding as anchor
points in representation space, which refines representations of different
classes to be well-separated from each other. Experiments on two datasets show
that the proposed model significantly outperforms strong baselines in both
one-shot and five-shot settings.
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